





import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 					A__       (				__lowercase					,    unittest.TestCase      ):




   """simple docstring"""



   __A  :							Dict			    =    ConsistencyModelPipeline
   __A  :							Union[str, Any]			    =    UNCONDITIONAL_IMAGE_GENERATION_PARAMS
   __A  :							str			    =    UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

   # Override required_optional_params to remove num_images_per_prompt
   __A  :							List[Any]			    =    frozenset(
       [
           '''num_inference_steps''',
           '''generator''',
           '''latents''',
           '''output_type''',
           '''return_dict''',
           '''callback''',
           '''callback_steps''',
       ]      )

   @property
   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          a__		:      str					=							UNetaDModel.from_pretrained(
              'diffusers/consistency-models-test'   ,    subfolder='test_unet'   ,    )
          return unet

   @property
   def 				__lowercase						(   self)     ->		Optional[int]:







          '''simple docstring'''

          a__		:      List[Any]					=							UNetaDModel.from_pretrained(
              'diffusers/consistency-models-test'   ,    subfolder='test_unet_class_cond'   ,    )
          return unet

   def 				__lowercase						(   self   ,    lowercase=False)     ->		Optional[int]:







          '''simple docstring'''

          if class_cond:
                 a__		:      str					=							self.dummy_cond_unet
          else:
                 a__		:      Dict					=							self.dummy_uncond_unet

          # Default to CM multistep sampler
          a__		:      Optional[Any]					=							CMStochasticIterativeScheduler(
              num_train_timesteps=40   ,    sigma_min=0.0_02   ,    sigma_max=80.0   ,    )

          a__		:      Optional[int]					=							{
              "unet": unet,
              "scheduler": scheduler,
          }

          return components

   def 				__lowercase						(   self   ,    lowercase   ,    lowercase=0)     ->		int:







          '''simple docstring'''

          if str(lowercase).startswith('mps'):
                 a__		:      Dict					=							torch.manual_seed(lowercase)
          else:
                 a__		:      Dict					=							torch.Generator(device=lowercase).manual_seed(lowercase)

          a__		:      str					=							{
              "batch_size": 1,
              "num_inference_steps": None,
              "timesteps": [22, 0],
              "generator": generator,
              "output_type": "np",
          }

          return inputs

   def 				__lowercase						(   self)     ->		str:







          '''simple docstring'''

          a__		:      Union[str, Any]					=							"cpu"  # ensure determinism for the device-dependent torch.Generator
          a__		:      Any					=							self.get_dummy_components()
          a__		:      Optional[int]					=							ConsistencyModelPipeline(**lowercase)
          a__		:      Dict					=							pipe.to(lowercase)
          pipe.set_progress_bar_config(disable=lowercase)

          a__		:      Optional[int]					=							self.get_dummy_inputs(lowercase)
          a__		:      str					=							pipe(**lowercase).images
          assert image.shape == (1, 32, 32, 3)

          a__		:      Optional[Any]					=							image[0, -3:, -3:, -1]
          a__		:      Optional[Any]					=							np.array([0.35_72, 0.62_73, 0.40_31, 0.39_61, 0.43_21, 0.57_30, 0.52_66, 0.47_80, 0.50_04])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3

   def 				__lowercase						(   self)     ->		Optional[Any]:







          '''simple docstring'''

          a__		:      Optional[Any]					=							"cpu"  # ensure determinism for the device-dependent torch.Generator
          a__		:      List[str]					=							self.get_dummy_components(class_cond=lowercase)
          a__		:      Union[str, Any]					=							ConsistencyModelPipeline(**lowercase)
          a__		:      List[str]					=							pipe.to(lowercase)
          pipe.set_progress_bar_config(disable=lowercase)

          a__		:      List[Any]					=							self.get_dummy_inputs(lowercase)
          a__		:      str					=							0
          a__		:      str					=							pipe(**lowercase).images
          assert image.shape == (1, 32, 32, 3)

          a__		:      Union[str, Any]					=							image[0, -3:, -3:, -1]
          a__		:      Any					=							np.array([0.35_72, 0.62_73, 0.40_31, 0.39_61, 0.43_21, 0.57_30, 0.52_66, 0.47_80, 0.50_04])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3

   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          a__		:      List[Any]					=							"cpu"  # ensure determinism for the device-dependent torch.Generator
          a__		:      str					=							self.get_dummy_components()
          a__		:      Tuple					=							ConsistencyModelPipeline(**lowercase)
          a__		:      int					=							pipe.to(lowercase)
          pipe.set_progress_bar_config(disable=lowercase)

          a__		:      Any					=							self.get_dummy_inputs(lowercase)
          a__		:      Optional[int]					=							1
          a__		:      Tuple					=							None
          a__		:      Union[str, Any]					=							pipe(**lowercase).images
          assert image.shape == (1, 32, 32, 3)

          a__		:      int					=							image[0, -3:, -3:, -1]
          a__		:      List[Any]					=							np.array([0.50_04, 0.50_04, 0.49_94, 0.50_08, 0.49_76, 0.50_18, 0.49_90, 0.49_82, 0.49_87])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3

   def 				__lowercase						(   self)     ->		Union[str, Any]:







          '''simple docstring'''

          a__		:      Optional[Any]					=							"cpu"  # ensure determinism for the device-dependent torch.Generator
          a__		:      Tuple					=							self.get_dummy_components(class_cond=lowercase)
          a__		:      str					=							ConsistencyModelPipeline(**lowercase)
          a__		:      Tuple					=							pipe.to(lowercase)
          pipe.set_progress_bar_config(disable=lowercase)

          a__		:      Optional[Any]					=							self.get_dummy_inputs(lowercase)
          a__		:      Optional[int]					=							1
          a__		:      str					=							None
          a__		:      Any					=							0
          a__		:      Optional[int]					=							pipe(**lowercase).images
          assert image.shape == (1, 32, 32, 3)

          a__		:      int					=							image[0, -3:, -3:, -1]
          a__		:      Any					=							np.array([0.50_04, 0.50_04, 0.49_94, 0.50_08, 0.49_76, 0.50_18, 0.49_90, 0.49_82, 0.49_87])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3





@slow
@require_torch_gpu
class 					A__       (				unittest.TestCase      ):




   """simple docstring"""



   def 				__lowercase						(   self)     ->		Dict:







          '''simple docstring'''

          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()

   def 				__lowercase						(   self   ,    lowercase=0   ,    lowercase=False   ,    lowercase="cpu"   ,    lowercase=torch.floataa   ,    lowercase=(1, 3, 64, 64))     ->		Optional[int]:







          '''simple docstring'''

          a__		:      Tuple					=							torch.manual_seed(lowercase)

          a__		:      Union[str, Any]					=							{
              "num_inference_steps": None,
              "timesteps": [22, 0],
              "class_labels": 0,
              "generator": generator,
              "output_type": "np",
          }

          if get_fixed_latents:
                 a__		:      Optional[int]					=							self.get_fixed_latents(seed=lowercase   ,    device=lowercase   ,    dtype=lowercase   ,    shape=lowercase)
                 a__		:      str					=							latents

          return inputs

   def 				__lowercase						(   self   ,    lowercase=0   ,    lowercase="cpu"   ,    lowercase=torch.floataa   ,    lowercase=(1, 3, 64, 64))     ->		Optional[Any]:







          '''simple docstring'''

          if type(lowercase) == str:
                 a__		:      int					=							torch.device(lowercase)
          a__		:      Dict					=							torch.Generator(device=lowercase).manual_seed(lowercase)
          a__		:      Optional[Any]					=							randn_tensor(lowercase   ,    generator=lowercase   ,    device=lowercase   ,    dtype=lowercase)
          return latents

   def 				__lowercase						(   self)     ->		Union[str, Any]:







          '''simple docstring'''

          a__		:      Optional[Any]					=							UNetaDModel.from_pretrained('diffusers/consistency_models'   ,    subfolder='diffusers_cd_imagenet64_l2')
          a__		:      int					=							CMStochasticIterativeScheduler(
              num_train_timesteps=40   ,    sigma_min=0.0_02   ,    sigma_max=80.0   ,    )
          a__		:      List[str]					=							ConsistencyModelPipeline(unet=lowercase   ,    scheduler=lowercase)
          pipe.to(torch_device=lowercase)
          pipe.set_progress_bar_config(disable=lowercase)

          a__		:      List[str]					=							self.get_inputs()
          a__		:      Any					=							pipe(**lowercase).images
          assert image.shape == (1, 64, 64, 3)

          a__		:      Union[str, Any]					=							image[0, -3:, -3:, -1]

          a__		:      Any					=							np.array([0.08_88, 0.08_81, 0.06_66, 0.04_79, 0.02_92, 0.01_95, 0.02_01, 0.01_63, 0.02_54])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2

   def 				__lowercase						(   self)     ->		Tuple:







          '''simple docstring'''

          a__		:      int					=							UNetaDModel.from_pretrained('diffusers/consistency_models'   ,    subfolder='diffusers_cd_imagenet64_l2')
          a__		:      List[str]					=							CMStochasticIterativeScheduler(
              num_train_timesteps=40   ,    sigma_min=0.0_02   ,    sigma_max=80.0   ,    )
          a__		:      str					=							ConsistencyModelPipeline(unet=lowercase   ,    scheduler=lowercase)
          pipe.to(torch_device=lowercase)
          pipe.set_progress_bar_config(disable=lowercase)

          a__		:      int					=							self.get_inputs()
          a__		:      Any					=							1
          a__		:      List[Any]					=							None
          a__		:      Optional[Any]					=							pipe(**lowercase).images
          assert image.shape == (1, 64, 64, 3)

          a__		:      List[str]					=							image[0, -3:, -3:, -1]

          a__		:      Union[str, Any]					=							np.array([0.03_40, 0.01_52, 0.00_63, 0.02_67, 0.02_21, 0.01_07, 0.04_16, 0.01_86, 0.02_17])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2

   @require_torch_a
   def 				__lowercase						(   self)     ->		Any:







          '''simple docstring'''

          a__		:      List[str]					=							UNetaDModel.from_pretrained('diffusers/consistency_models'   ,    subfolder='diffusers_cd_imagenet64_l2')
          a__		:      List[Any]					=							CMStochasticIterativeScheduler(
              num_train_timesteps=40   ,    sigma_min=0.0_02   ,    sigma_max=80.0   ,    )
          a__		:      Tuple					=							ConsistencyModelPipeline(unet=lowercase   ,    scheduler=lowercase)
          pipe.to(torch_device=lowercase   ,    torch_dtype=torch.floataa)
          pipe.set_progress_bar_config(disable=lowercase)

          a__		:      Union[str, Any]					=							self.get_inputs(get_fixed_latents=lowercase   ,    device=lowercase)
          # Ensure usage of flash attention in torch 2.0
          with sdp_kernel(enable_flash=lowercase   ,    enable_math=lowercase   ,    enable_mem_efficient=lowercase):
                 a__		:      Tuple					=							pipe(**lowercase).images
          assert image.shape == (1, 64, 64, 3)

          a__		:      List[Any]					=							image[0, -3:, -3:, -1]

          a__		:      str					=							np.array([0.18_75, 0.14_28, 0.12_89, 0.21_51, 0.20_92, 0.14_77, 0.18_77, 0.16_41, 0.13_53])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3

   @require_torch_a
   def 				__lowercase						(   self)     ->		Dict:







          '''simple docstring'''

          a__		:      Optional[Any]					=							UNetaDModel.from_pretrained('diffusers/consistency_models'   ,    subfolder='diffusers_cd_imagenet64_l2')
          a__		:      Optional[int]					=							CMStochasticIterativeScheduler(
              num_train_timesteps=40   ,    sigma_min=0.0_02   ,    sigma_max=80.0   ,    )
          a__		:      List[str]					=							ConsistencyModelPipeline(unet=lowercase   ,    scheduler=lowercase)
          pipe.to(torch_device=lowercase   ,    torch_dtype=torch.floataa)
          pipe.set_progress_bar_config(disable=lowercase)

          a__		:      str					=							self.get_inputs(get_fixed_latents=lowercase   ,    device=lowercase)
          a__		:      Tuple					=							1
          a__		:      Union[str, Any]					=							None
          # Ensure usage of flash attention in torch 2.0
          with sdp_kernel(enable_flash=lowercase   ,    enable_math=lowercase   ,    enable_mem_efficient=lowercase):
                 a__		:      Tuple					=							pipe(**lowercase).images
          assert image.shape == (1, 64, 64, 3)

          a__		:      str					=							image[0, -3:, -3:, -1]

          a__		:      List[Any]					=							np.array([0.16_63, 0.19_48, 0.22_75, 0.16_80, 0.12_04, 0.12_45, 0.18_58, 0.13_38, 0.20_95])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3




import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowercase    :   Tuple						=			logging.getLogger(__name__)




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:Dict	,  a						:Union[str, Any]=-1		)						->					List[str]:
								# in NER datasets, the last column is usually reserved for NER label
								__UpperCamelCase      :  str       =   label_idx







	def   _lowerCamelCase							(   self						:str	,  a						:Tuple	,  a						:Union[Split, str]		)						->					List[InputExample]:
								if isinstance(a	,  a		):
															__UpperCamelCase      :  Dict       =   mode.value
								__UpperCamelCase      :  Union[str, Any]       =   os.path.join(a	,  f'{mode}.txt'		)
								__UpperCamelCase      :  Any       =   1
								__UpperCamelCase      :  List[str]       =   []
								with open(a	,  encoding="utf-8"		) as f:
															__UpperCamelCase      :  Tuple       =   []
															__UpperCamelCase      :  Dict       =   []
															for line in f:
																						if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
																													if words:
																																				examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
																																				guid_index += 1
																																				__UpperCamelCase      :  Dict       =   []
																																				__UpperCamelCase      :  List[str]       =   []
																						else:
																													__UpperCamelCase      :  Union[str, Any]       =   line.split(" "		)
																													words.append(splits[0]		)
																													if len(a		) > 1:
																																				labels.append(splits[self.label_idx].replace("\n"	,  ""		)		)
																													else:
																																				# Examples could have no label for mode = "test"
																																				labels.append("O"		)
															if words:
																						examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
								return examples







	def   _lowerCamelCase							(   self						:int	,  a						:TextIO	,  a						:TextIO	,  a						:List		)						->					Optional[Any]:
								__UpperCamelCase      :  Tuple       =   0
								for line in test_input_reader:
															if line.startswith("-DOCSTART-"		) or line == "" or line == "\n":
																						writer.write(a		)
																						if not preds_list[example_id]:
																													example_id += 1
															elif preds_list[example_id]:
																						__UpperCamelCase      :  Union[str, Any]       =   line.split()[0] + " " + preds_list[example_id].pop(0		) + "\n"
																						writer.write(a		)
															else:
																						logger.warning("Maximum sequence length exceeded: No prediction for '%s'."	,  line.split()[0]		)







	def   _lowerCamelCase							(   self						:Tuple	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						__UpperCamelCase      :  List[str]       =   f.read().splitlines()
															if "O" not in labels:
																						__UpperCamelCase      :  Any       =   ["O"] + labels
															return labels
								else:
															return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:List[str]		)						->					int:
								# in CONLL2003 dataset chunk column is second-to-last
								super().__init__(label_idx=-2		)







	def   _lowerCamelCase							(   self						:List[str]	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						__UpperCamelCase      :  Optional[Any]       =   f.read().splitlines()
															if "O" not in labels:
																						__UpperCamelCase      :  int       =   ["O"] + labels
															return labels
								else:
															return [
															    "O",
															    "B-ADVP",
															    "B-INTJ",
															    "B-LST",
															    "B-PRT",
															    "B-NP",
															    "B-SBAR",
															    "B-VP",
															    "B-ADJP",
															    "B-CONJP",
															    "B-PP",
															    "I-ADVP",
															    "I-INTJ",
															    "I-LST",
															    "I-PRT",
															    "I-NP",
															    "I-SBAR",
															    "I-VP",
															    "I-ADJP",
															    "I-CONJP",
															    "I-PP",
															]




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def   _lowerCamelCase							(   self						:List[str]	,  a						:Union[str, Any]	,  a						:Union[Split, str]		)						->					List[InputExample]:
								if isinstance(a	,  a		):
															__UpperCamelCase      :  Optional[Any]       =   mode.value
								__UpperCamelCase      :  List[str]       =   os.path.join(a	,  f'{mode}.txt'		)
								__UpperCamelCase      :  Dict       =   1
								__UpperCamelCase      :  List[str]       =   []

								with open(a	,  encoding="utf-8"		) as f:
															for sentence in parse_incr(a		):
																						__UpperCamelCase      :  Optional[int]       =   []
																						__UpperCamelCase      :  int       =   []
																						for token in sentence:
																													words.append(token["form"]		)
																													labels.append(token["upos"]		)
																						assert len(a		) == len(a		)
																						if words:
																													examples.append(InputExample(guid=f'{mode}-{guid_index}'	,  words=a	,  labels=a		)		)
																													guid_index += 1
								return examples







	def   _lowerCamelCase							(   self						:List[Any]	,  a						:TextIO	,  a						:TextIO	,  a						:List		)						->					str:
								__UpperCamelCase      :  List[Any]       =   0
								for sentence in parse_incr(a		):
															__UpperCamelCase      :  Tuple       =   preds_list[example_id]
															__UpperCamelCase      :  Dict       =   ""
															for token in sentence:
																						out += f'{token["form"]} ({token["upos"]}|{s_p.pop(0		)}) '
															out += "\n"
															writer.write(a		)
															example_id += 1







	def   _lowerCamelCase							(   self						:List[str]	,  a						:str		)						->					List[str]:
								if path:
															with open(a	,  "r"		) as f:
																						return f.read().splitlines()
								else:
															return [
															    "ADJ",
															    "ADP",
															    "ADV",
															    "AUX",
															    "CCONJ",
															    "DET",
															    "INTJ",
															    "NOUN",
															    "NUM",
															    "PART",
															    "PRON",
															    "PROPN",
															    "PUNCT",
															    "SCONJ",
															    "SYM",
															    "VERB",
															    "X",
															]